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Mixtures of common factor analyzers for high-dimensional data with missing information
Wang, Wan-Lun - In: Journal of Multivariate Analysis 117 (2013) C, pp. 120-133
observation. Practical issues related to the specification of initial values, model-based clustering and discriminant procedure …
Persistent link: https://www.econbiz.de/10010665708
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Clustering of functional data in a low-dimensional subspace
Yamamoto, Michio - In: Advances in Data Analysis and Classification 6 (2012) 3, pp. 219-247
functional objects and also find an optimal subspace for clustering, simultaneously. The method is based on the k-means criterion … for functional data and seeks the subspace that is maximally informative about the clustering structure in the data. An …
Persistent link: https://www.econbiz.de/10010846119
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On the upper bound of the number of modes of a multivariate normal mixture
Ray, Surajit; Ren, Dan - In: Journal of Multivariate Analysis 108 (2012) C, pp. 41-52
matrices, which in turn provides a clear guideline as to when one can use mixture analysis for clustering high dimensional data. …
Persistent link: https://www.econbiz.de/10010572294
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Factor models for large and incomplete data sets with unknown group structure
Camacho, Maximo; López-Buenache, Germán - In: International journal of forecasting 39 (2023) 3, pp. 1205-1220
Persistent link: https://www.econbiz.de/10014465266
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Partial dissimilarities with application to clustering
Brossier, Gildas - In: Journal of Classification 11 (1994) 1, pp. 37-58
Persistent link: https://www.econbiz.de/10005602931
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Sample Size Estimation for Longitudinal Designs with Attrition: Comparing Time-Related Contrasts Between Two Groups
Hedeker, Donald; Gibbons, Robert D.; Waternaux, Christine - In: Journal of Educational and Behavioral Statistics 24 (1999) 1, pp. 70-93
Formulas for estimating sample sizes are presented to provide specified levels of power for tests of significance from a longitudinal design allowing for subject attrition. These formulas are derived for a comparison of two groups in terms of single degree-of-freedom contrasts of population...
Persistent link: https://www.econbiz.de/10010776007
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FogLearn: Leveraging Fog-Based Machine Learning for Smart System Big Data Analytics
Barik, Rabindra K.; Priyadarshini, Rojalina; Dubey, … - In: International Journal of Fog Computing (IJFC) 1 (2018) 1, pp. 15-34
-means clustering in Ganga River Basin Management and real-world feature data for detecting diabetes patients suffering from diabetes …
Persistent link: https://www.econbiz.de/10012044784
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Leveraging knowledge discovery and knowledge visualization to define the "inner areas" : an application to an Italian province
Moretto, Valentino; Elia, Gianluca; Ghiani, Gianpaolo - In: Journal of knowledge management 26 (2022) 10, pp. 2743-2771
Persistent link: https://www.econbiz.de/10013455080
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Weighty evidence? : poverty estimation with missing data
Drèze, Jean; Somanchi, Anmol - In: Studies in microeconomics 12 (2024) 1, pp. 93-106
Persistent link: https://www.econbiz.de/10014635289
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An analysis and visualization methodology for identifying and testing market structure
France, Stephen L.; Ghose, Sanjoy - In: Marketing science : the marketing journal of the … 35 (2016) 1, pp. 182-197
Persistent link: https://www.econbiz.de/10011437863